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检索条件"主题词=3D from Multi-view and Sensors"

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NeuWigs: A Neural dynamic Model for Volumetric Hair Capture and Animation

NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture ...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Ziyan Nam, Giljoo Stuyck, Tuur Lombardi, Stephen Cao, Chen Saragih, Jason Zollhofer, Michael Hodgins, Jessica Lassner, Christoph Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta Real Labs Redmond WA USA

ISBN: (纸本)9798350301298

The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality. Both problems are highly challenging, because hair has complex geometry and appearance and exhibits challenging motion. In this paper, we present a two-stage approach that models hair independently of the head to address these challenges in a data-driven manner. The first stage, state compression, learns a low-dimensional latent space of 3d hair states including motion and appearance via a novel autoencoder-as-a-tracker strategy. To better disentangle the hair and head in appearance learning, we employ multi-view hair segmentation masks in combination with a differentiable volumetric renderer. The second stage optimizes a novel hair dynamics model that performs temporal hair transfer based on the discovered latent codes. To enforce higher stability while driving our dynamics model, we employ the 3d point-cloud autoencoder from the compression stage for de-noising of the hair state. Our model outperforms the state of the art in novel view synthesis and is capable of creating novel hair animations without relying on hair observations as a driving signal

关键词： 3d from multi-view and sensors

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Frequency-Modulated Point Cloud Rendering with Easy Editing

Frequency-Modulated Point Cloud Rendering with Easy Editing

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Yi Huang, Xiaoyang Ni, Bingbing Li, Teng Zhang, Wenjun Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Anhui Univ Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

We develop an effective point cloud rendering pipeline for novel view synthesis, which enables high fidelity local detail reconstruction, real-time rendering and user-friendly editing. In the heart of our pipeline is an adaptive frequency modulation module called Adaptive Frequency Net (AFNet), which utilizes a hypernetwork to learn the local texture frequency encoding that is consecutively injected into adaptive frequency activation layers to modulate the implicit radiance signal. This mechanism improves the frequency expressive ability of the network with richer frequency basis support, only at a small computational budget. To further boost performance, a preprocessing module is also proposed for point cloud geometry optimization via point opacity estimation. In contrast to implicit rendering, our pipeline supports high-fidelity interactive editing based on point cloud manipulation. Extensive experimental results on NeRF-Synthetic, ScanNet, dTU and Tanks and Temples datasets demonstrate the superior performances achieved by our method in terms of PSNR, SSIM and LPIPS, in comparison to the state-of-the-art. Code is released at https://***/yizhangphd/FreqPCR.

关键词： 3d from multi-view and sensors

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NeRF-dS: Neural Radiance Fields for dynamic Specular Objects

NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yan, Zhiwen Li, Chen Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9798350301298

dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of rendering photo-realistic novel view images from a monocular RGB video of a dynamic scene. Although it warps moving points across frames from the observation spaces to a common canonical space for rendering, dynamic NeRF does not model the change of the reflected color during the warping. As a result, this approach often fails drastically on challenging specular objects in motion. We address this limitation by reformulating the neural radiance field function to be conditioned on surface position and orientation in the observation space. This allows the specular surface at different poses to keep the different reflected colors when mapped to the common canonical space. Additionally, we add the mask of moving objects to guide the deformation field. As the specular surface changes color during motion, the mask mitigates the problem of failure to find temporal correspondences with only RGB supervision. We evaluate our model based on the novel view synthesis quality with a self-collected dataset of different moving specular objects in realistic environments. The experimental results demonstrate that our method significantly improves the reconstruction quality of moving specular objects from monocular RGB videos compared to the existing NeRF models. Our code and data are available at the project website (1).

关键词： 3d from multi-view and sensors

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LoGoNet: Towards Accurate 3d Object detection with Local-to-Global Cross-Modal Fusion

LoGoNet: Towards Accurate 3D Object Detection with Local-to-...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Xin Ma, Tao Hou, Yuenan Shi, Botian Yang, Yuchen Liu, Youquan Wu, Xingjiao Chen, Qin Li, Yikang Qiao, Yu He, Liang East China Normal Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Hsch Bremerhaven Bremerhaven Germany Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9798350301298

LidAR-camera fusion methods have shown impressive performance in 3d object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and point cloud features are fused across the whole scene. Such practice lacks fine-grained region-level information, yielding suboptimal fusion performance. In this paper, we present the novel Local-to-Global fusion network (LoGoNet), which performs LidAR-camera fusion at both local and global levels. Concretely, the Global Fusion (GoF) of LoGoNet is built upon previous literature, while we exclusively use point centroids to more precisely represent the position of voxel features, thus achieving better cross-modal alignment. As to the Local Fusion (LoF), we first divide each proposal into uniform grids and then project these grid centers to the images. The image features around the projected grid points are sampled to be fused with position-decorated point cloud features, maximally utilizing the rich contextual information around the proposals. The Feature dynamic Aggregation (FdA) module is further proposed to achieve information interaction between these locally and globally fused features, thus producing more informative multi-modal features. Extensive experiments on both Waymo Open dataset (WOd) and KITTI datasets show that LoGoNet outperforms all state-of-the-art 3d detection methods. Notably, LoGoNet ranks 1st on Waymo 3d object detection leaderboard and obtains 81.02 mAPH (L2) detection performance. It is noteworthy that, for the first time, the detection performance on three classes surpasses 80 APH (L2) simultaneously. Code will be available at https://***/sankin97/LoGoNet.

关键词： 3d from multi-view and sensors

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Hierarchical Supervision and Shuffle data Augmentation for 3d Semi-Supervised Object detection

Hierarchical Supervision and Shuffle Data Augmentation for 3...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Chuandong Gao, Chenqiang Liu, Fangcen Li, Pengcheng Meng, deyu Gao, Xinbo Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing Peoples R China Chongqing Key Lab Signal & Informat Proc Chongqing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Macau Univ Sci & Technol Taipa Macao Peoples R China

ISBN: (纸本)9798350301298

State-of-the-art 3d object detectors are usually trained on large-scale datasets with high-quality 3d annotations. However, such 3d annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudo-labeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle data Augmentation (HSSdA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSdA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://***/azhuantou/HSSdA.

关键词： 3d from multi-view and sensors

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Panoptic Lifting for 3d Scene Understanding with Neural Fields

Panoptic Lifting for 3D Scene Understanding with Neural Fiel...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Siddiqui, Yawar Porzi, Lorenzo Bulo, Samuel Rota Mueller, Norman Niessner, Matthias dai, Angela Kontschieder, Peter Tech Univ Munich Munich Germany Meta Real Labs Zurich Zurich Switzerland

ISBN: (纸本)9798350301298

We propose Panoptic Lifting, a novel approach for learning panoptic 3d volumetric representations from images of in-the-wild scenes. Once trained, our model can render color images together with 3d-consistent panoptic segmentation from novel viewpoints. Unlike existing approaches which use 3d input directly or indirectly, our method requires only machine-generated 2d panoptic segmentation masks inferred from a pre-trained network. Our core contribution is a panoptic lifting scheme based on a neural field representation that generates a unified and multi-view consistent, 3d panoptic representation of the scene. To account for inconsistencies of 2d instance identifiers across views, we solve a linear assignment with a cost based on the model's current predictions and the machine-generated segmentation masks, thus enabling us to lift 2d instances to 3d in a consistent way. We further propose and ablate contributions that make our method more robust to noisy, machine-generated labels, including test-time augmentations for confidence estimates, segment consistency loss, bounded segmentation fields, and gradient stopping. Experimental results validate our approach on the challenging Hypersim, Replica, and ScanNet datasets, improving by 8.4, 13.8, and 10.6% in scene-level PQ over state of the art.

关键词： 3d from multi-view and sensors

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PointClustering: Unsupervised Point Cloud Pre-training using Transformation Invariance in Clustering

PointClustering: Unsupervised Point Cloud Pre-training using...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Long, Fuchen Yao, Ting Qiu, Zhaofan Li, Lusong Mei, Tao HiDream Ai Inc JD Explore Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

Feature invariance under different data transformations, i.e., transformation invariance, can be regarded as a type of self-supervision for representation learning. In this paper, we present PointClustering, a new unsupervised representation learning scheme that leverages transformation invariance for point cloud pre-training. PointClustering formulates the pretext task as deep clustering and employs transformation invariance as an inductive bias, following the philosophy that common point cloud transformation will not change the geometric properties and semantics. Technically, PointClustering iteratively optimizes the feature clusters and backbone, and delves into the transformation invariance as learning regularization from two perspectives: point level and instance level. Point-level invariance learning maintains local geometric properties through gathering point features of one instance across transformations, while instance-level invariance learning further measures clusters over the entire dataset to explore semantics of instances. Our PointClustering is architecture-agnostic and readily applicable to MLP-based, CNN-based and Transformer-based backbones. We empirically demonstrate that the models pre-learnt on the ScanNet dataset by PointClustering provide superior performances on six benchmarks, across downstream tasks of classification and segmentation. More remarkably, PointClustering achieves an accuracy of 94.5% on ModelNet40 with Transformer backbone. Source code is available at https://***/FuchenUSTC/PointClustering.

关键词： 3d from multi-view and sensors

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Progressive Neighbor Consistency Mining for Correspondence Pruning

Progressive Neighbor Consistency Mining for Correspondence P...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Xin Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

The goal of correspondence pruning is to recognize correct correspondences (inliers) from initial ones, with applications to various feature matching based tasks. Seeking neighbors in the coordinate and feature spaces is a common strategy in many previous methods. However, it is difficult to ensure that these neighbors are always consistent, since the distribution of false correspondences is extremely irregular. For addressing this problem, we propose a novel global-graph space to search for consistent neighbors based on a weighted global graph that can explicitly explore long-range dependencies among correspondences. On top of that, we progressively construct three neighbor embeddings according to different neighbor search spaces, and design a Neighbor Consistency block to extract neighbor context and explore their interactions sequentially. In the end, we develop a Neighbor Consistency Mining Network (NCMNet) for accurately recovering camera poses and identifying inliers. Experimental results indicate that our NCMNet achieves a significant performance advantage over state-of-the-art competitors on challenging outdoor and indoor matching scenes. The source code can be found at https://***/xinliu29/NCMNet.

关键词： 3d from multi-view and sensors

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Aedet: Azimuth-invariant multi-view 3d Object detection

AeDet: Azimuth-invariant Multi-view 3D Object Detection

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Feng, Chengjian Jie, Zequn Zhong, Yujie Chu, Xiangxiang Ma, Lin Meituan Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Recent LSS-based multi-view 3d object detection has made tremendous progress, by processing the features in Brid-Eye-view (BEV) via the convolutional detector. However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization. To preserve the inherent property of the BEV features and ease the optimization, we propose an azimuth-equivariant convolution (AeConv) and an azimuth-equivariant anchor. The sampling grid of AeConv is always in the radial direction, thus it can learn azimuth-invariant BEV features. The proposed anchor enables the detection head to learn predicting azimuth-irrelevant targets. In addition, we introduce a camera-decoupled virtual depth to unify the depth prediction for the images with different camera intrinsic parameters. The resultant detector is dubbed Azimuth-equivariant detector (Aedet). Extensive experiments are conducted on nuScenes, and Aedet achieves a 62.0% NdS, surpassing the recent multi-view 3d object detectors such as PETRv2 and BEVdepth by a large margin. Project page: https://***/aedet.

关键词： 3d from multi-view and sensors

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RefSR-NeRF: Towards High Fidelity and Super Resolution view Synthesis

RefSR-NeRF: Towards High Fidelity and Super Resolution View ...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Huang, Xudong Li, Wei Hu, Jie Chen, Hanting Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis. despite NeRF's extraordinary success in the neural rendering field, it suffers from blur in high resolution rendering because its inherent multilayer perceptron struggles to learn high frequency details and incurs a computational explosion as resolution increases. Therefore, we propose RefSR-NeRF, an end-to-end framework that first learns a low resolution NeRF representation, and then reconstructs the high frequency details with the help of a high resolution reference image. We observe that simply introducing the pre-trained models from the literature tends to produce unsatisfied artifacts due to the divergence in the degradation model. To this end, we design a novel lightweight RefSR model to learn the inverse degradation process from NeRF renderings to target HR ones. Extensive experiments on multiple benchmarks demonstrate that our method exhibits an impressive trade-off among rendering quality, speed, and memory usage, outperforming or on par with NeRF and its variants while being 52x speedup with minor extra memory usage. Code will be available at: Mindspore and Pytorch

关键词： 3d from multi-view and sensors

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